Multi-objective Interval Optimization Approach for Energy Hub Planning With Consideration of Renewable Energy and Demand Response Synergies

被引：0

作者：

Zeng B. ^{[1
]}

Xu F. ^{[2
]}

Liu Y. ^{[1
]}

Gong D. ^{[3
]}

Zhu X. ^{[1
]}

机构：

[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing

[2] State Grid Lianyungang Power Supply Company, Lianyungang

[3] School of Information and Control Engineering, China University of Mining and Technology, Xuzhou

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2021年 / 41卷 / 21期

基金：

中国国家自然科学基金;

关键词：

Demand response; Integrated energy system; Multi-objective interval optimization; Renewable energy; Uncertainty;

D O I：

10.13334/j.0258-8013.pcsee.201303

中图分类号：

学科分类号：

摘要：

Improving the consumption of renewable energy source (RES) to promote low carbon energy use is an important task for the development of integrated energy systems. Hence, from the perspective of source-load synergy, this paper proposed a multi-objective interval optimization approach for energy hub (EH) planning, which aimed to efficiently use RESs under demand response uncertainty. The model aimed to minimize the system economic costs while maximizing the utilization of RES, by comprehensively considering the capacity allocation of the hub components and demand-side management strategy. Besides, an interval-based method was used to account for the uncertainties of RES and demand response. The proposed model was transformed into a deterministic one by using the order relation and the possibility degree approach for interval numbers and the non-dominated sorting genetic algorithm-II was employed to solve the problem. The simulation results show that the proposed method can effectively improve the efficiency of RES utilization and the economics of investment; in addition, it can flexibly meet different planning requirements, therefore, it is supposed to have greater value in engineering practice. © 2021 Chin. Soc. for Elec. Eng.

引用

页码：7212 / 7224

页数：12

共 35 条

[1]

JIA Hongjie, WANG Dan, XU Xiandong, Et al., Research on some key problems related to integrated energy systems, Automation of Electric Power Systems, 39, 7, pp. 198-207, (2015)

[2]

SHAO Chengcheng, WANG Xifan, WANG Xiuli, Et al., Probe into analysis and planning of multi-energy systems, Proceedings of the CSEE, 36, 14, pp. 3817-3828, (2016)

[3]

GEIDL M, KOEPPEL G, FAVRE-PERROD P, Et al., Energy hubs for the future, IEEE Power and Energy Magazine, 5, 1, pp. 24-30, (2007)

[4]

MA Tengfei, WU Junyong, HAO Liangliang, Et al., The optimal structure planning and energy management strategies of smart multi energy systems, Energy, 160, pp. 122-141, (2018)

[5]

DOLATABADI A, MOHAMMADI-IVATLOO B, ABAPOUR M, Et al., Optimal stochastic design of wind integrated energy hub, IEEE Transactions on Industrial Informatics, 13, 5, pp. 2379-2388, (2017)

[6]

LIN Zihan, LIU Zuoyu, WEN Fushuan, Et al., Optimal placement of energy hubs considering N-1 security criterion, Electric Power Automation Equipment, 39, 8, pp. 137-143, (2019)

[7]

SHAO Changzheng, DING Yi, WANG Jianhui, Et al., Modeling and integration of flexible demand in heat and electricity integrated energy system, IEEE Transactions on Sustainable Energy, 9, 1, pp. 361-370, (2018)

[8]

QIU Zhi, WANG Beibei, HUANG Junhui, Et al., Optimal configuration and sizing of regional energy service company's energy hub with integrated demand response, IEEJ Transactions on Electrical and Electronic Engineering, 14, 3, pp. 383-393, (2019)

[9]

HEMMATI S, GHADERI S F, GHAZIZADEH M S., Sustainable energy hub design under uncertainty using Benders decomposition method, Energy, 143, pp. 1029-1047, (2018)

[10]

ZENG Dan, YAO Jianguo, YANG Shengchun, Et al., Probabilistic load flow calculation considering price-elasticity load uncertainties, Automation of Electric Power Systems, 39, 20, pp. 66-71, (2015)

← 1 2 3 4 →